The integrated pedestrian simulation model proposed in this paper allows us to develop a more realistic simulation of pedestrian behaviors at a shopping mall. In particular, consideration of vision of each individual allows us to mimic physical and psychological interactions among the people and the environment more realistically. Similarly, consideration of extended Decision Field Theory allowed us to represent human decision deliberation process. In addition, consideration of a rich set of attributes for the environment as well as people has allowed us to mimic a real shopping mall environment closely. The constructed simulation using AnyLogic software was utilized to conduct several experiments on performance of the mall and scalability of the proposed model.

The paperwork presented by Victor Romanov, Dmitry Yakovlev and Anna Lelchuk describes modification of wealth distribution among the customers. A simulation model created by them helps to analyze the following interrelated spheres, such as labour market, stock market, enterprise investment strategy, tax level.

This interdisciplinary research focuses on improving the oral health of older adults as a means of enhancing their overall wellbeing and quality of life. Periodontal disease is a risk factor for other chronic illnesses, notably diabetes and cardiovascular disease. In order to identify policies that improve oral health for older adults, a dynamic modeling approach that considers community and individual level factors is utilized.

In this paper, we investigate output accuracy for a Discrete Event Simulation (DES) model and Agent Based Simulation (ABS) model. The purpose of this investigation is to find out which of these simulation techniques is the best one for modelling human reactive behaviour in the retail sector. In order to study the output accuracy in both models, we have carried out a validation experiment in which we compared the results from our simulation models to the performance of a real system. Our experiment was carried out using a large UK department store as a case study.

A novel approach to represent learning in human decision behavior for evacuation scenarios is proposed under the context of an extended Belief-Desire-Intention framework. In particular, we focus on how a human adjusts his perception process (involving a Bayesian belief network) in Belief Module dynamically against his performance in predicting the environment as part of his decision planning function. To this end, a Q-learning algorithm (reinforcement learning algorithm) is employed and further developed.

In recent US Census data widely reported in the press “Hispanics” have become the largest minority group in the US. Using simulation modeling technology we look at some of the structural forces that shape the characteristics of the Hispanic population. The model creates a simulated Hispanic population whose level of acculturation to the broader population of which it is a part dynamically varies according to individual choice. The modeling technique used draws on both System Dynamic and Agent based paradigms both supported by innovative AnyLogic software. The representative Hispanic population is disaggregated down to the individual level as individual agents. Each agent makes choices stochastically as modulated by its current state and the outside environment that it is in.